1. Field of the Invention
The present invention relates generally to a system and method for recognizing temporal events using enhanced temporal decision trees that act on features of data over a time window.
2. Description of the Related Art
In machine learning, a decision tree is a predictive model that classifies or maps observations to thereby recognize events. The structure of a traditional decision tree is illustrated at FIG. 1. The features, c1 through CN, represent variables to be tested at the nodes of the decision tree, and the result of each test determines if another feature is tested at a node one level lower or if a classification is made. For example, the result of a test of the feature c5 at node 1-1 leads to either another test at node 2-1 or at node 2-2. The result of a test of the feature c3 at node 2-2 leads to either a classification or another test at node 3-1, which is one level lower on the decision tree than node 2-2.
As discussed in the paper, Kwon D., et al., “Online Touch Behavior Recognition of Hard-cover Robot Using Temporal Decision Tree Classifier,” Journal of Artificial Intelligence Research, In Proceedings of IEEE RO-MAN 2008 (2008), the traditional decision tree has been further developed to account for classifications that are based on temporal or time-related features. The Kwon, et al. paper describes a temporal decision tree for classifying the type of touch given to a robot. The section detailing multi-windowing feature extraction explains that four features, force, contact time, repetition, and changes in contact area, are used to characterize the touch and that each of the features is calculated after a certain period of sampling. For example, force is calculated based on 20 acceleration data samples. The sample window within which the 20 acceleration data samples are acquired is moved every 10 ms. Thus, force(1) is calculated based on 20 acceleration data samples in the first sample window, and force(n) is calculated based on 20 acceleration data samples in the nth sample window.
The structure of a standard temporal decision tree is illustrated at FIG. 2. The standard temporal decision tree, depicted at FIG. 2, differs from the traditional decision tree, depicted at FIG. 1, by using temporal features such that the test at each node is of a feature at a given time in a time sequence. For example, the value of feature c3 at t-t′ is tested at node 2/2-1. In relation to the Kwon, et al. paper discussed above, for example, a node may test force(3) at a node.